The aviation industry largely depends on the reliable functioning of critical information technology (IT) infrastructure. Like many other industries, the aviation industry is challenged with providing adequate security for such IT infrastructure and mitigating the effects of any cyber events. Examples of cyber events include malicious or suspicious events that compromise, or attempt to compromise, the operation of an aircraft's network, including its data connections, data transmission, and computing systems.
Many scientific, engineering and IT security applications need a lot of random-looking numbers, which cannot be distinguished from true random by standard statistical tests. Generally, if the numbers are generated by an algorithm, which is performed in software on a microprocessor or in specialized electronic hardware, they are referred to as pseudorandom numbers. Pseudorandom numbers are useful in applications such as simulating physical systems, whitening structured data, and randomized computing for solving numerical mathematical problems. In IT security applications, pseudorandom numbers are used in protocols, such as nonces (numbers used once), initial values (IV), data hiding, and cryptographic key derivation.
Traditionally, pseudorandom numbers have been generated on microprocessors, which take several clock cycles for each byte to be generated. The fastest practical algorithms generate 8 bytes (64 bits) in 4 . . . 12 clock cycles on 64 bit processors. In some applications, this generation time for pseudorandom numbers is too long, such as when large quantities of pseudorandom numbers are needed in short periods of time. In some instances, electronic hardware can be custom designed to speed up the algorithms. However, such prior art pseudorandom number generators that incorporate the use of a microprocessor produce low quality (correlated, biased) numbers, are slow, tie up the microprocessor with the task, clog memory buses, and consume a lot of power/energy (resulting in increased heat dissipation and battery drain).
An additional drawback of prior art pseudorandom number generators is that they are unprotected from side channel attacks, which creates significant risks in IT security applications. During operation of electronic devices that are processing data, some information about secret keys or sensitive data always leaks in side channels (physical sources of unintended information dissemination), including data dependent variation of response times, fluctuation of power use, or ultrasonic or electromagnetic wave radiation. This is why the secrecy and integrity of stored or transmitted data can generally be assured by cryptographic means when no adversary has physical access to the electronic devices.
Thus, it is desirable to have an improved pseudorandom number generator that generates many pseudorandom numbers in one clock cycle and enables improvements in security by reducing side channel leakage without significantly increasing processing time, system complexity, the size of electronic circuits, or energy usage.